Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Masakuna, Jordan F."'
Autor:
Masakuna, Jordan F., Nkashama, DJeff Kanda, Soltani, Arian, Frappier, Marc, Tardif, Pierre-Martin, Kabanza, Froduald
Training data sets intended for unsupervised anomaly detection, typically presumed to be anomaly-free, often contain anomalies (or contamination), a challenge that significantly undermines model performance. Most robust unsupervised anomaly detection
Externí odkaz:
http://arxiv.org/abs/2408.07718
Autor:
Masakuna, Jordan F., Kroon, Steve
This paper is concerned with distributed detection of central nodes in complex networks using closeness centrality. Closeness centrality plays an essential role in network analysis. Evaluating closeness centrality exactly requires complete knowledge
Externí odkaz:
http://arxiv.org/abs/2106.14011
We propose a method for combining probabilistic outputs of classifiers to make a single consensus class prediction when no further information about the individual classifiers is available, beyond that they have been trained for the same task. The la
Externí odkaz:
http://arxiv.org/abs/2009.00565
Autor:
Masakuna, Jordan F.
This essay reviews human observer-based methods employed in shark spotting in Muizenberg Beach. It investigates Machine Learning methods for automated shark detection with the aim of enhancing human observation. A questionnaire and interview were use
Externí odkaz:
http://arxiv.org/abs/1905.13309
The problem of coordination without a priori information about the environment is important in robotics. Applications vary from formation control to search and rescue. This paper considers the problem of search by a group of solitary robots: self-int
Externí odkaz:
http://arxiv.org/abs/1905.07434
Publikováno v:
Applied Network Science; 2/17/2023, Vol. 8 Issue 1, p1-20, 20p